Operation Analysis of the Floating Derrick for Offshore Wind Turbine Installation Based on Machine Learning

To investigate the influencing factors on the operation of an offshore wind turbine installation ship, a neural network, as a machine-learning method, is built to predict and analyze the motion response of a floating derrick in the process of a lifting operation under an external environmental load. The numerical method for the double floating body, from the software SESAM/SIMA, is validated against the experiments. The numerical method is used to establish the floating derrick-lifting impeller model to obtain the motions of the ship and impeller and the coupling effect. Based on the numerical results, the BP neural network model is built to predict the ship’s operation. The results show that the BP neural network model for the floating derrick and impeller motion prediction is very feasible. Combined with the Rules for Lifting Appliances of Ships and Offshore Installations and the Noble Denton Guidelines for Marine Lifting Operations, the operation of the floating crane system can be determined based on the environmental parameters.